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Clinical decision support system for breathlessness.

Anthony Sunjaya1,2, Christine R Jenkins1,2,3

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Summary
This summary is machine-generated.

Clinical decision support systems (CDSS) show promise in aiding breathlessness assessment and management. While AI-driven CDSS can augment clinical judgment, real-world benefits require further high-quality studies.

Keywords:
artificial intelligencebreathlessnessclinical decision support system

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Clinical Decision Support

Background:

  • Clinical decision support systems (CDSS) encompass rule-based and AI-driven tools.
  • These systems aim to aid clinicians in diagnosis, management, and patient education.

Purpose of the Study:

  • To review evidence on CDSS, including potential benefits, concerns, evaluation methods, and future directions.
  • To explore the role of AI in augmenting clinical prediction for conditions causing breathlessness.

Main Methods:

  • Systematic review of high-level evidence on CDSS.
  • Analysis of published evidence, clinical experience, and expert insights.

Main Results:

  • CDSS demonstrate potential in assessing breathlessness, improving diagnostic test interpretation, and personalizing care.
  • Model development studies show clinically acceptable performance in differentiating causes of breathlessness (e.g., COPD, heart failure).
  • Real-world clinical benefits and the translation of model performance to practice remain largely unknown, with limited trial evidence.

Conclusions:

  • AI-powered CDSS can enhance clinical judgment, potentially improving patient and population care, especially in resource-constrained settings.
  • Limitations and applicability of CDSS must be considered.
  • High-quality clinical practice studies are essential to validate CDSS effectiveness.